Enhancing machine learning with data-efficient methods
Abstract/Contents
- Abstract
- Supervised deep learning techniques have made a tremendous and unprecedented impact in all segments of our lives, including finance, healthcare, social networks, and more. However, the progress is hindered by a substantial challenge: the dependence on large, high-quality labeled datasets. This issue is particularly acute in areas such as biomedicine, where the procurement and annotation of data are not only costly but also intricate. In response to these challenges, this thesis introduces innovative machine learning strategies that are data-efficient, aiming to reduce the dependence on extensive labeled datasets while either preserving or improving the efficacy of deep learning models. The thesis is systematically divided into two primary sections, each targeting key aspects of data-efficient machine learning. Part I is dedicated to the development of advanced algorithms optimized for existing datasets, particularly under the constraint of limited labeling. This section introduces a novel machine learning setting for enhancing generalization and robustness in low-label scenarios, proposes an innovative open-world semi-supervised learning framework, and adapts this framework to real-world applications. Part II focuses on augmenting training resources by incorporating supplementary knowledge. It explores the integration of auxiliary tasks to enhance training, examines the use of historical data to improve AutoML search efficiency, and introduces methods for including large datasets that were previously unmanageable due to memory constraints.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2024; ©2024 |
| Publication date | 2024; 2024 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Cao, Kaidi |
|---|---|
| Degree supervisor | Leskovec, Jurij |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Koyejo, Oluwasanmi |
| Thesis advisor | Ma, Tengyu |
| Degree committee member | Koyejo, Oluwasanmi |
| Degree committee member | Ma, Tengyu |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Kaidi Cao. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2024. |
| Location | https://purl.stanford.edu/mt928bm8533 |
Access conditions
- Copyright
- © 2024 by Kaidi Cao
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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